Optimization and individualization of medication selection and dosing

ABSTRACT

Described are methods for treating a patient with a therapeutic drug using a combination of genetic and non-genetic information to tailor the dose of the drug to the patient.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation Application of and claims thebenefit of priority to U.S. application Ser. No. 15/367,950, filed Dec.2, 2016, which is a Continuation Application of and claims the benefitof priority to U.S. application Ser. No. 14/053,220, filed Oct. 14,2013, which is a Continuation Application of and claims the benefit ofpriority to U.S. application Ser. No. 12/085,606, filed Jan. 13, 2009,which is a national stage entry under 37 C.F.R. § 371 of InternationalApplication Ser. No. PCT/US2006/045631, filed Nov. 28, 2006, whichclaims the benefit of priority to U.S. Provisional Patent Application,Ser. No. 60/740,430, filed Nov. 29, 2005 and of U.S. Provisional PatentApplication, Ser. No. 60/783,118, filed Mar. 16, 2006, the disclosuresof which are incorporated herein by reference in their entireties.

FIELD OF THE INVENTION

This invention relates to methods for combining a patient's geneticinformation, a patient's non-heritable host factors and candidatemedication characteristics to optimize and individual medication dosageand compound selection.

BACKGROUND OF THE INVENTION

One of the most important but unresolved problems in therapy with potentand often toxic drugs has been the lack of our ability to describe,understand, and quantify the important mechanistic relationships andvariability between drug doses, concentrations in blood, concentrationsof metabolites in other body compartments, and the therapeutic and toxicdrug effects. For the most part, defining drug action and inter-patientvariability has been limited to simplistic, less informativedescriptions of average maximum and minimum drug does requirements thatdo not permit true individualization of therapy for each patient.

For some drugs over 90% of the measurable variation in selectedpharmacokinetic parameters has been shown to be heritable. Traditionallyin pharmacokinetic (PK) analysis a series of concentrations over time ismeasured. A structural model is defined and fit to the data in order toobtain estimates of the desired parameters such as clearance and volumeof distribution. The model is fitted to the individual data by using aleast squares algorithm that minimizes the difference between observedand the model predicted concentrations. For reasons of simplicity theassumption is made that differences between the observed and predictedconcentrations are caused by random error. With this traditional type ofanalysis, a model is defined for each subject and the individualparameters are then summarized across individuals. However, imprecisionin the sample mean and sample standard deviation frequently are greaterthan expected, while estimates of variability in these parameters arenot well characterized.

The Food and Drug Administration (FDA) is recognizing the importance ofthe genetic contribution to the inter-individual variation in responseto therapy. There has been a significant increase in the number of newdrug applications sent to the FDA containing pharmacogenetic information(Wendy Chou, Ph.D./FDA Apr. 3, 2003). Two package inserts reflect thistrend. Thioridazine (Mellaril) which is used for neuropsychiatricconditions is contraindicated in patients who are CYP2D6 poormetabolizers; this warning is specifically stated in two places in theinsert. Similarly in multiple places in the package insert forAtomoxetine (Strattera, a medication used for attention deficithyperactivity disorder (ADHD)), the association between geneticpolymorphisms in drug metabolism and adverse drug reactions is stated.

In certain ethnic groups as many as 10% of the adolescent populationhave a CYP2D6 haplotype that is associated with poor metabolism of manyantidepressant medications. See Wong et al. (2001) Ann. Acad. Med.Singapore, 29:401-406. Clinical genomic testing of these individuals hasclear implications for their treatment and prognosis. In extreme cases,children who were poor metabolizers and who were not identified have hadtragic outcomes. These negative case reports have included a reporteddeath of a nine-year-old boy who was not recognized to be a poor CYP2D6metabolizer. The treatment of this child with fluoxetine continueddespite the development of multiple symptoms because these symptoms werenot recognized as being related to bis extremely high serum levels offluoxetine. Sallee et al. (2000) J. Child Adol. Psychiatry, 10(1):27-34.

Adverse drug reactions occur in 28% of hospitalized patients and in 17%of hospitalized children. In a report by Phillips et al. in JAMA, 27drugs were most frequently cited in adverse drug reaction reports. 59% (16/27) of these drugs were metabolized by at least one enzyme having apoor metabolizer genotype. 37% ( 11/27) were metabolized by CYP2D6,specifically drugs acting on the central nervous system. The annual costof the morbidity and mortality associated with adverse drug reaction is$177,000,000 dollars (Year 2000 dollars). Clearly drug toxicity is amajor health issue with 100,000 deaths a year and 2,000,000 personssuffering permanent disability or prolonged hospitalizations as a resultof direct medication adverse reactions.

Although significant inter-individual variability exists in the responseto most medications, medication selection and titration is usuallyempiric rather than individualized. The main reason that physicians donot incorporate genetic and non-heritable host factors responsible forthis inter-individual variability into treatment plans is the lack ofapplicable, easy to use algorithms that translate the patient'scharacteristics into clinical recommendations. Thus there is a need inthe art for a pharmacokinetic dose individualization technique that isinformative, cost saving, and effective.

SUMMARY OF THE INVENTION

The present invention is concerned generally with the field ofidentifying appropriate medications and treatment regimens for a diseasebased upon genotype in mammals, particularly in humans. It is furtherconcerned with the genetic basis of inter-patient variation in responseto therapy, including drug therapy. Specifically, the inventiondescribes the use of gene sequence variances for optimizing efficacy andsafety of drug therapy. The invention relates to computerized methodsand/or computer-assisted methods for identifying patient populationsubsets that respond to drug therapy similarly.

The invention provides computerized methods and/or computer-assistedmethods of targeting drug therapy, particularly dosing regimens andcompound selection to an individual subject or patient. The methodsincorporate genetic and non-heritable factors into drug selection andtitration. The invention provides computational algorithms forrecommending a dosing regimen for a particular patient utilizingpopulation models, genotype information, and clinical information. Themethods of the invention allow iterative integration of patientinformation and clinical data. The methods of the invention providetimely, easy to understand, and easy to implement recommendations.Further the invention provides proactive identification of patientspotentially requiring more in depth assessment by a clinicalpharmacology specialist.

It is therefore a first aspect of the present invention to provide acomputerized method and/or computer-assisted method of selecting adosing regimen for a patient the method that includes the steps of: (a)integrating patient data with patient associated genotype information;(b) generating a drag concentration profile for the patient; (c)integrating the drug concentration profile and the target drugconcentration profile; and (d) providing a dosing regimen for a firstcompound likely to result in the target drug concentration profile inthe subject. In a more detailed embodiment, the method further includesthe steps of (x) providing a biological sample; (y) monitoring abiomarker in the biological sample; and (z) integrating the biomarkervalue with the drug concentration profile information. Alternatively orin addition, the patient data may comprise patient demographic data andclinical data. Alternatively or in addition, the clinical data mayinclude information regarding a second compound, where the secondcompound may modulate metabolism of the first compound. Alternatively orin addition, the first compound may be a neuropsychiatric medication.Alternatively or in addition, the method may further comprise the stepof determining the genotype of a patient at one or more loci ofinterest.

It is a second object of the present invention to provide a computerizedmethod and/or computer-assisted method for selecting a dosing regimenfor a patient, where the method includes the steps of: (a) obtainingpatient data; (b) obtaining patient associated genotype information; (c)integrating the patient data with the patient associated genotypeinformation; (d) generating a drug concentration profile for thepatient; (e) integrating the drug concentration profile and a targetdrug concentration profile; (f) providing a dosing regimen for thecompound likely to result in the target drug concentration profile inthe subject; (g) providing a biological sample from the patient; (h)monitoring a biomarker in the biological sample; (i) integrating thebiomarker value with the drug concentration profile information; G)generating a second drug concentration profile for the patient; (k)supplying a second target drug concentration profile; (l) providing asecond dosing regimen for the compound likely to result in the secondtarget drug concentration profile. In addition, the method may furtherinclude the step of performing the processes of (f) through (l) at leasta second time. Alternatively or in addition, the method may furtherinclude the step of selecting a population model for the patient.Alternatively or in addition, the method may further include the step ofgenerating a probability value for a designated response by the patient.

It is a third aspect of the present invention to provide a computerizedmethod and/or computer-assisted method of selecting a dosing regimen fora patient, where the method includes the steps of: (a) generatingstatistical population models of drug interactions for a plurality ofgenotypes; (b) obtaining patient associated genotype information; and(c) establishing a dosing regimen by applying the genotype informationagainst the population models. In addition, the step of generatingpopulation models may include the use of Bayesian algorithms.Alternatively or in addition, the population models of drug interactionsmay be defined for a combination of genotypes and non-geneticinformation.

It is a fourth aspect of the present invention to provide a computerizedmethod and/or computer-assisted method for selecting one or more drugsfor a patient that includes the steps of: identifying the phenotype;providing a first plurality of possible medications based upon theidentified phenotype; and calculating a ranked list or a predictiveindex of medications from the first plurality of medications based upon,at least in part, patient specific genetic factors, non-heritablepatient factors and drug specific factors. In addition, the calculatingstep may further consider one or more preclinical toxicity variables,one or more pharmacokinetic variables, one or more clinical efficacyvariables, one or more clinical toxicity variables, one or more clinicalsafety issues, and/or one or more ease of use/adherence variables. Inaddition, in the calculating step, one or more of the followingvariables could contribute linearly: TI (therapeutic index—the ratio of(50% lethal dose/50% therapeutic dose)=measure of the drug's inherenttoxicity); F (Bioavailability=fraction of the dose which reaches thesystemic circulation as intact drug); fu (the extent to which a drug isbound in plasma or blood is called the fraction unbound=[unbound drugconcentration/[total drug concentration]); f-BIND-T (fraction of drugthat is a substrate for a drug-specific efflux transporter “T”); MET-L(drug with linear metabolism); f-MET-E (fraction of drug that ismetabolized by drag metabolizing enzyme “E”); PEX (percentage of drugmetabolizing enzyme “E” with functional polymorphism “X”); CL_(cr)(creatinine clearance=the volume of blood cleared of creatinine per unittime=(liters/hour)); IDR (rate of idiosyncratic reactions); FORM(formulation); FREQ (frequency of daily drug administration); MAT ED(maternal education level); SES (socio-economic class); and TRANS(method of transportation to/from clinic). Alternatively or in addition,in the calculating step, one or more of the following variables couldcontribute exponentially: ATA (number of functional non-wild typetransporter polymorphisms for the specific patient); MET-NonL (drug withnon-linear metabolism); AEA (number of functional non-wild type drugmetabolizing enzyme polymorphisms for the specific patient); MED-IND(concurrent use of medications that induce metabolizing enzymes);MED-INH (concurrent use of medications that inhibit metabolizingenzymes); DIET-IND (concurrent use of dietary supplements that inducemetabolizing enzymes); DIET-INH (concurrent use of dietary supplementsthat inhibit metabolizing enzymes); NNT-EFF (number need to treat=numberof patients who need to be treated to reach 1 desired outcome); META-EEF(results from an efficacy meta-analysis of clinical trials involvingmedications used to treat a neuropsychiatric disorder); NNT-TOX (numberneed to treat=number of patients who need to be treated to have a 1toxicity outcome); and META-TOX (results from toxicity meta-analysis ofclinical trials involving medications used to treat a neuropsychiatricdisorder).

In another alternative detailed embodiment of the fourth aspect of thepresent invention, the calculating step may involve linear algebracomputational science to integrate disease specific evidence basedmedicine data, drug specific basic pharmacology characteristics, patientspecific advanced pharmacology principles, and/or patient specificenvironmental and genetic factors to produce a ranking of potentialmedications. In addition, or alternatively, the calculating step mayassign, for each potential medication, computational valuescorresponding to a favorability of utilizing the potential medicationfor a corresponding plurality of factors. In addition, the plurality offactors may include factors from a plurality of the followingcategories: disease specific evidence based medicine data, drug specificbasic pharmacology characteristics, patient specific advancedpharmacology principles, patient specific environmental and patientspecific genetic factors. Alternatively or in addition, the plurality ofcomputational values may include positive values for favorable factorsand negative values for unfavorable factors, and the calculating stepinvolves adding the computational values to determine a score.Alternatively or in addition, the plurality of computational values mayinclude positive values for favorable factors and negative values forunfavorable factors and weights corresponding to the relative importanceof such factors, and the calculating step involves adding the weightedcomputational values to determine a score.

In yet another alternate detailed embodiment of the invention, thecomputerized method may further comprise a step of generating anadherence score corresponding to a predicted likelihood that the patientwill adhere to a scheduled therapy or prescription.

It is a fifth aspect of the present invention to provide a computerizedmethod and/or computer-assisted method for selecting a starting dose ofa medication for a patient that includes the steps of: for a givenmedication, determining if the patient is an extensive metabolizer forthe medication, an intermediate metabolizer for the medication, or apoor metabolizer for the medication; calculating the starting dose basedupon, at least in part, a usual drug dose for a given population (Dpop),the frequency of extensive metabolizers in the given population(f_(EM)), the frequency of intermediate metabolizers in the givenpopulation (f_(IM)) and/or the frequency of poor metabolizers in thegeneral population (f_(PM)); and determining a minimal dose adjustmentunit for the medication based, at least in part, upon the patient'sgenetic information. In addition, the step of determining if the patientmay be an extensive metabolizer for the medication, an intermediatemetabolizer for the medication, or a poor metabolizer for the medicationis based, at least in part, upon the patient's genetic information.Alternatively or in addition, (a) the percent of the usual drug doseDpop for an extensive metabolizer DEM is

DEM=100/(fnM+f _(IM) ·S+f _(PM) ·R)

where S is the Area Under the Time Concentration Curve for extensivemetabolizer subpopulation divided by the Area Under the TimeConcentration Curve for intermediate metabolizer subpopulation, andwhere R is the Area Under the Time Concentration Curve for extensivemetabolizer subpopulation divided by the Area Under the TimeConcentration Curve for poor metabolizer subpopulation; (b) the percentof the usual drug dose D_(pop) for a poor metabolizer D_(PM) is

D _(PM) =R·D _(EM); and

(c) the percent of the usual drug dose D_(pop) for an intermediatemetabolizer D_(IM) is

D _(IM) =S·D _(EM)

Alternatively or in addition, the minimal dose adjustment unit for themedication may be based, at least in part, upon a number ofnon-functional alleles, D_(EM), D_(IM), and/or D_(PM).

It is a sixth aspect of the present invention to provide a computerizedmethod and/or computer-assisted method for selecting one or more drugsfor a patient that includes the steps of: identifying the phenotype;providing a first plurality of possible medications based upon thepatient's diagnosis; and calculating a ranked list or a predictive indexof medications from the first plurality of medications based upon, atleast in part, patient specific genetic factors, non-heritable patientfactors and drug specific factors. In addition, the calculating step mayinvolve linear algebra computational science to integrate diseasespecific evidence based medicine data, drug specific basic pharmacologycharacteristics, patient specific advanced pharmacology principles,and/or patient specific environmental and genetic factors to produce aranking of potential medications. Alternatively or in addition, thecalculating step assigns, for each potential medication, computationalvalues corresponding to a favorability of utilizing the potentialmedication for a corresponding plurality of factors, where the pluralityof factors may include factors from a plurality of the followingcategories: disease specific evidence based medicine data, drug specificbasic pharmacology characteristics, patient specific advancedpharmacology principles, patient specific environmental and patientspecific genetic factors. Alternatively or in addition, the plurality ofcomputational values include positive values for favorable factors andnegative values for unfavorable factors, and the calculating stepinvolves adding the computational values to determine a score, where theplurality of computational values may include positive values forfavorable factors and negative values for unfavorable factors andweights corresponding to the relative importance of such factors, andthe calculating step involves adding the weighted computational valuesto determine a score.

In another detailed embodiment of the sixth aspect of the presentinvention, the method may include a step of generating an adherencescore corresponding to a predicted likelihood that the patient willadhere to a scheduled therapy or prescription.

It is a seventh aspect of the present invention to provide a computer, acomputer system or a computerized tool designed and programmed toperform any or all of the above computer implemented methods. Inaddition, the computer, computer system or computerized tool may providea graphical user interface to provide for the collection of appropriatedata from users, such as any of the above-discussed factors.Alternatively, or in addition, the computer, computer system orcomputerized tool may provide a graphical user interface (or any otherknown computer output, such as a printout) to provide the report,analysis, recommendation or any other output resulting from any of theabove-discussed methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 presents a schematic depiction of the processes involved in amethod selecting a dosing regimen for an individual patient.

FIGS. 2A-C present risperidone pharmacokinetic profiles for threedifferent dosing regimens for a particular patient. FIG. 2A depicts anexemplary pharmacokinetic model-based simulation of the risperidoneconcentration time profile. FIG. 2B depicts an exemplary pharmacokineticmodel-based simulation of the risperidone concentration time profileafter altering the dosing regimen. FIG. 2C depicts an exemplarypharmacokinetic model-based simulation of the risperidone concentrationtime profile with a third dosing regimen. In each panel a solid lineindicates the patient's compound concentration predicted by the methodsof the invention in each dosing regimen and the broken line indicatesthe therapeutic range, in this example arbitrarily chosen to be between3 and 10 ng/mL. The observed biomarker value is indicated with solidcircles or triangles.

FIG. 3 is an example (very small) segment of a disease matrix for usewith an exemplary embodiment of the invention.

FIG. 4 is a screen shot illustrating a step of an exemplary computerimplemented method of the present invention.

FIG. 5 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 6 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 7 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 8 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 9 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 10 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 11 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 12 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 13 is a screen shot illustrating another step of an exemplarycomputer implemented method of the present invention.

FIG. 14 is a screen shot illustrating an output report/analysisgenerated by an exemplary computer implemented method of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

Defining and describing the often complex relationships of drug actionand inter-patient variability has historically been very difficult.Developing pharmacokinetic (PK) and pharmacodynamic (PD) models of thesevariables provides a method of defining and describing the relationshipsbetween drug action and patient variability. Further drug or compoundactions (effects) are directly related to the drug concentration at thesite(s) of action. There is usually a better relationship between theeffect of a given drug and its concentration in the blood than betweenthe dose of the drug given and the effect.

The invention provides population models for various compounds thatincorporate pharmacokinetic and pharmacodynamic models of drug actionand interpatient variability. Further the invention providescomputerized methods and/or computer-assisted methods (includingsoftware algorithms) that utilize the one or more population models ofthe invention to predict a dosing regimen for a particular compound orto predict patient response to a compound. The computerized methodsand/or computer-assisted methods (including software algorithms) of theinvention generate a prediction regarding a subject's ability tometabolize a compound of interest. The computerized methods and/orcomputer-assisted methods (including software algorithms) of theinvention provide for iterative evaluation of a patient's response to adosing regimen or compound incorporating data obtained from monitoringat least one suitable biomarker. Often subjects receive more than onemedication. These additional medications may affect the subject'sability to metabolize a compound of interest. Thus, in an embodimentcomputerized methods and/or computer-assisted methods (includingsoftware algorithms) of the invention provide a means of integratinginformation regarding such an additional compound or compounds and theeffects of such an additional compound on the subject's ability tometabolize a compound of interest.

A “compound” comprises, but is not limited to, a drug, medication,agent, therapeutically effective agent, neuropsychiatric medications,neurotransmitter inhibitors, neurotransmitter receptor modulators,G-proteins, G-protein receptor inhibitors, ACE inhibitors, hormonereceptor modulators, alcohols, reverse transcriptase inhibitors, nucleicacid molecules, aldosterone antagonists, polypeptides, peptides,peptidomimetics, glycoproteins, transcription factors, small molecules,chemokine receptors, antisense nucleotide sequences, chemokine receptorligands, lipids, antibodies, receptor inhibitors, ligands, sterols,steroids, hormones, chemokine receptor agonists, chemokine receptorantagonists, agonists, antagonists, ion-channel modulators, diuretics,enzymes, enzyme inhibitors, carbohydrates, deaminases, deaminaseinhibitors, hormones, phosphatases, lactones, and vasodilators. Acompound may additionally comprise a pharmaceutically acceptablecarrier.

Neuropsychiatric medications include, but are not limited to,antidepressants, mood elevating agents, norepinephrine-reuptakeinhibitors, tertiary amine tricyclics, amitriptyline, clomipramine,doxepin, imipramine, secondary amine tricyclics amoxapine, desipramine,maprotiline, protriptyline, nortriptyline, selective serotonin-reuptakeinhibitors (SSRIs), fluoxetine, fluvoxamine, paroxetine, sertraline,citalopram, escitalopram, venlafaxine, atypical antidepressants,bupropion, nefazodone, trazodone; noradrenergic and specificserotonergic antidepressants, mirtazapine, monoamine oxidase inhibitors,phenelzine, tranylcypromine, selegiline; antipsychotic agents, tricyclicphenothiazines, chlorpromazine, triflupromazine, thioridazine,mesoridazine, fluphenazine, trifluoperazine, thioxanthenes,chlorprothixene, clopenthixol, flupenthixol, piflutixol, thiothixene,dibenzepines, loxapine, clozapine, clothiapine, metiapine, zotapine,fluperlapine, olanzapine, butyrophenones, haloperidol,diphenylbutylpiperidines, fluspirilene, penfluridol, pimozide,haloperidol decanoate, indolones, neuroleptics, anti-anxiety/sedativeagents, benzodiazepines, chlordiazepoxide, diazepam, oxazepam,clorazepate, lorazepam, prazepam, alprazolam, and halazepam; moodstabilizing agents, lithium salts, valproic acid; attention deficithyperactivity disorder agents, dextroamphetamine, methylphenidate,pemoline, and atomoxetine; anticonvulsants, phenobarbital, phenytoin,carbamazepine, valproic acid, felbamate, gabapentin, tiagabine,lamotrigine, topiramate, zonisamide, oxcarbazepine, levetiracetam,pregabalin, ethotoin, and peganone; headache medications, ibuprofen,aspirin/acetaminophen/caffeine, diclofenac, ketoprofen, ketorolac,flurbiprofen, meclofenamate, naproxen, ergotamine tartrate,dihydroergotamine, ergotamine, acetaminophen/isometheptenemucate/dichloralphenazone, sumatriptan succinate, zolmitriptan,rizatriptan, naratriptan hydrochloride, almotriptan, frovatriptan,eletriptan, diclofenac, fenoprofen, flurbiprofen, kepaprofen, naproxensodium, amitriptyline, desipramine, doxepin, imipramine, nortriptyline,fluoxetine, paroxetine, sertraline, venlafaxine, trazodone, bupropion,atenolol, metoprolol, nadolol, propranolol, timolol, diltiazem,nicardipine, nifedipine, nimodipine, verapamil, divalproex sodium,gabapentin, valproic acid, and topiramate; and dementia medications,tacrine, donepezil, galantamine, galanthamine, rivastigmine, andmemantine.

By “drug” is intended a chemical entity, biological product, orcombination of chemical entities or biological products administered toa person to treat, prevent, or control a disease or condition. The term“drug” may include, without limitation, agents that are approved forsale as pharmaceutical products by government regulatory agencies suchas the U.S. Food and Drug Administration, European Medicines EvaluationAgency, agents that do not require approval by a government regulatoryagency, food additives or supplements including agents commonlycharacterized as vitamins, natural products, and completely orincompletely characterized mixtures of chemical entities includingnatural agents or purified or partially purified natural products. It isunderstood that the methods of the invention are suitable for use withany of the drugs or compounds in the 2005 Physicians Desk Reference,Thomson Healthcare 59^(th) ed., herein incorporated by reference in itsentirety.

The computerized methods and/or computer-assisted methods (includingsoftware algorithms) of the invention utilize subject or patientassociated genotype information. The term “genotype” refers to thealleles present in genomic DNA from a subject or patient where an allelecan be defined by the particular nucleotide(s) present in a nucleic acidsequence at a particular site(s). Often a genotype is the nucleotide(s)present at a single polymorphic site known to vary in the humanpopulation. By “genotype information” is intended information pertainingto variances or alterations in the genetic structure of a gene or locusof interest. Genotype information may indicate the presence or absenceof a predetermined allele. A “loci of interest” may be a gene, allele,or polymorphism of interest. Genes or loci of interest include genesthat encode a) medication specific metabolizing enzymes, b) medicationspecific transporters, c) medication specific receptors, d) enzymes,transporters or receptors affecting other drugs that interact with themedication in question or e) body functions that affect that activitiesof the medication in question. In an embodiment of the invention loci ofinterest include, but are not limited to, five cytochrome P450 genes,the serotonin transporter gene, the dopamine transporter gene, and thedopamine receptor genes. The five cytochrome P450 genes can encodeCYP2D6, CYP1A2, CYP2C19, CYP2C9 and CYP2E1. Alleles of particularinterest include, but are not limited to, the CYP1A2*1A or 1A2*3 allele,the CYP2C19*1A, 2C19*1B, or 2C19*2A allele, and the CYP2D6*1A, 2D6*2,2D6*2N, 2D6*3, 2D6*4, 2D6*5, 2D6*6, 2D6*7, 2D6*8, 2D6*10, 2D6*12, or2D6*17 allele. The serotonin receptor genes encode serotonin receptorsIA, IB, ID, 2A, or 2C and the dopamine receptor genes encode dopaminereceptors D1, D2, D3, D4, D5, and D6. The serotonin transported gene isalso an important part of the genotype. Additional genes, alleles,polymorphisms, and loci of interest are presented in Tables 1 and 2.

TABLE 1 CYTOCHROME P450 GENES Cytochrome P450Gene Allele Polymorphism1A1  *1A None  *2 A2455G  *3 T3205C  *4 C2453A 1A2  *1A None  *1F−164C > a  *3 G1042A 1B1  *1 None  *2 R48G  *3 L432V  *4 N453S *11 V57C*14 E281X *18 G365W *19 P379L *20 E387K *25 R469W 2A6  *1A None  *1BCYP2A 7 translocated to 3′ - end  *2 T479A  *5 *1B + G6440T 2B6  *1 *1′2R22C *1′3 S259C  *4 K262R  *5 R487C  *6 Q172H; K262R  *7 Q172H; I <262R; R487C 2C8  *1A None  *1B −271C > A  *1C −370T > G  *2 I269F  *3R139K; K399R  *4 I264M 2C9  *1 None  *2 R144C  *3 I359L  *5 D360E 2C18rot T204A m2 A460T 2C19  *1A None  *1B I331V  *2A Splicing defect  *2BSplicing defect; E92D  *3 New stop codon 636G > A  *4 GTG initiationcodon, 1A > G *5(A, B) 1297C > T, amino acid change (R433W)  *6 395G?A,amino acid change (R132Q)  *7 IVS5 + 2T > A, splicing defect  *8 358T >C, amino acid change (WI20R) 2D6  *1A None  *2 G1661C, C2850T  *2N Geneduplication  *3 A2549 deletion  *4 G1846A  *5 Gene deletion  *6 T1707deletion  *7 A2935C  *8 G1758T *10 C100T *12 G124A *17 C1023TCIO23T,C2850T *35 G31A 2E1  *1A None *1C, *1D (6 or 8 bp repeats)  *2 G1132A *4 G476A  *5 G(−1293)C  *5 C−(1053)T  *7 T(−333)A  *7 G(−71)T  *7A(−353)G 3A4  *1A None  *1B A(−392)G  *2 Amino acid change (S222P)  *5Amino acid change (P218R)  *6 Frameshift, 831 ins A *12 Amino acidchange (L373F) *13 Amino acid change (P416L) *15A Amino acid change(RI62Q) *17 Amino acid change (F1892, Decreased) *18A Amino acid change(L293P, increased) 3A5  *1A None  *3 A6986G  *5 T12952C  *6 G14960A

TABLE 2 NON-CYTOCHROME P450 GENES Gene Symbol Polymorphism DopamineTransporter DATI, 40 bp VNTR SLC6A3 10 repeat allele G710A, Q237R C124T,L42F Dopamine Receptor D1 DRDI DRD 1 B2 T244G C179T G127A T11G C81TT5950, S199A G150T, R50S C1100, T37R AI09C, T37P Dopamine Receptor D2DRD2 TaqI A A1051G, T35A C932G, S311 C C928, P31 OS G460A, V1541Dopamine Receptor D3 DRD3 Ball in exon I MspI DRD31 Gly/Ser (allele 2)A250, S9G Dopamine Receptor D4 DRD4 48 repeat in exon 3 7 repeat allele.12/13 bp insertion/deletion T581G, V194G C841G, P281A Dopamine ReceptorD5 DRD5 T978C L88F A889C, T297P G1252A, V4181 G181A, V61M G185C, C62ST2630, R88L G1354A, W455 Tryptophan TPH A218C Hydroxylase A779C G-5806TA-6526G (CT)m(CAMCT)p allele 194 in 3′ UTR, 5657 bp distant from exonSerotonin Transporter 5-HTTR Promoter repeat (44 bp insertion(L)/deletion(S) (L = Long form; S = Short form) Exon 2 variable repeatA1815C G603C G167C Serotonin Receptor 1A HTR1A RsaI G815A, G272D G656T,R219L C548T, P551L A82G, 128V G64A, G22S C47T, P16L Serotonin Receptor1B HTR1B G861C G816C, V287V T371G, F124C T655C, F219L A1099G, I367VG1120A, E374K Serotonin Receptor 1D HTR1D G506T C173T C794T, S265LSerotonin Receptor 2A HTR2A C74A T102C T516C C1340T C1354T SerotoninReceptor 2C HTR2C G796C ClOG, L4V G68C, C23S Catechol-o- COMT G158A(Also known methyltransferase as Val/Met) G214T A72S G101C C34S G473A

In an embodiment of the invention, the computerized methods and/orcomputer-assisted methods (including software algorithms) are utilizedto select a dosing regimen for a patient in need of a neuropsychiatricmedication. A major gene in the neuropsychiatric panel is CYP2D6.Substrates of CYP2D6 typically are weak bases with the cationic bindingsite located away from the carbon atom to be oxidized. In particular,substrates of CYP2D6 include amitriptyline, nortriptyline, haloperidol,and desipramine. Some individuals have altered CYP2D6 gene sequencesthat result in synthesis of enzymes devoid of catalytic activity or inenzymes with diminished catalytic activity. These individuals metabolizeSSRIs and tricyclic antidepressants (TCAs) poorly.Duplication/multiplication of the functional CYP2D6 gene also has beenobserved and results in ultrarapid metabolism of SSRIs and other drugs.Individuals without inactivating polymorphisms, deletions, orduplications have the phenotype of an extensive drug metabolizer and aredesignated as CYF2D6*1. The CYP2D6*3 and *4 alleles account for nearly70% of the total deficiencies that result in the poor metabolizerphenotype. The polymorphism responsible for CYP2D6*3 (2549A>del)produces a frame-shift in the mRNA. A polymorphism involved with theCYP2D6*4 allele (1846G>A) disrupts mRNA splicing. These changes producetruncated forms of CYP2D6 devoid of catalytic activity. Other poormetabolizers are CYP2D6*5, *10, and *17. CYP2D6*5 is due to completegene deletion. The polymorphisms in CYF2D6*10 and *17 produce amino acidsubstitutions in the CYP2D6 enzyme which have decreased enzyme activity.All of these polymorphisms are autosomal co-dominant traits. Onlyindividuals who are homozygous or who are compound heterozygous forthese polymorphisms are poor metabolizers. Individuals who areheterozygous, with one normal gene and one polymorphic gene, will havemetabolism intermediate between the extensive (normal) and poormetabolizers. Individuals who are heterozygous forduplication/multiplication alleles are ultra-rapid metabolizers.

CYP1A2 metabolizes many aromatic and heterocyclic anilines includingclozapine and imipraniline. The CYP1A2*IF allele can result in a productwith higher inducibility or increased activity. (See Sachse et al.(1999) Br. J. Clin. Pharmacol. 47:445-449). CYP2C19 also metabolizesmany substrates including imipramine, citalopram, and diazepam. TheCYP2C19*2A, *2B, *3, *4, *SA, *5B, *6, *7, and ‘:’8 alleles encodeproducts with little or no activity. See Theanu et al. (1999) J.Pharmacol. Exp. Ther. 290: 635-640.

CYP1A1 can be associated with toxic or allergic reactions byextra-hepatic generation of reactive metabolites. CYP3A4 metabolizes avariety of substrates including alprazolam. CYP1B1 can be associatedwith toxic or allergic reactions by extra-hepatic generation of reactivemetabolites and also metabolizes steroid hormones (e.g., 17-estradiol).Substrates for CYP2A6 and CYP2B6 include valproic acid and bupropion,respectively. Substrates for CYP2C9 include Tylenol and antabuse(disulfuram). Substrates for CYP2E1 include phenytoin and carbamazepine.Decreases in activity in one or more of the cytochrome P450 enzymes canimpact one or more of the other cytochrome P450 enzymes.

Methods of determining genotype information are known in the art.Genotype information obtained by any method of determining genotypeknown in the art may be employed in the practice of the invention. Anymeans of determining genotype known in the art may be used in themethods of the invention.

Generally genomic DNA is used to determine genotype, although mRNAanalysis has been used as a screening method in some cases. Routine,commercially available methods can be used to extract genomic DNA from ablood or tissue sample such as the QIAamp® Tissue Kit (Qiagen,Chatsworth, Calif.), Wizard® Genomic DNA Purification IDT (Promega) andthe A.S.A.P.™ Genomic DNA Isolation Kit (Boehringer Mannheim,Indianapolis, Ind.).

Typically before the genotype is determined, enzymatic amplification ofthe DNA segment containing the loci of interest is performed. A commontype of enzymatic amplification is the polymerase chain reaction (PCR).Known methods of PCR include, but are not limited to, methods usingpaired primers, nested primers, single specific primers, degenerateprimers, gene-specific primers, vector-specific primers,partially-mismatched primers, and the like. Known methods of PCRinclude, but are not limited to, methods using DNA polymerases fromextremophiles, engineered DNA polymerases, and long-range PCR. It isrecognized that it is preferable to use high fidelity PCR reactionconditions in the methods of the invention. See also Innis et al, eds.(1990) PCR Protocols: A Guide to Methods and Applications (AcademicPress, New York); Innis and Gelfand, eds. (1995) PCR Strategies(Academic Press, New York); Innis and Gelfand, eds. (1999) PCR MethodsManual (Academic Press, New York); and PCR Primer: A Laboratory ManualEd. by Dieffenbach, C. and Dveksler, G., Cold Spring Harbor LaboratoryPress, 1995. Long range PCR amplification methods include methods suchas those described in the TaK.aRa LA PCR guide, Takara Shuzo Co., Ltd.

When using RNA as a source of template, reverse transcriptase can beused to synthesize complementary DNA (cDNA) strands. Ligase chainreaction, strand displacement amplification, self-sustained sequencereplication or nucleic acid sequence-based amplification also can beused to obtain isolated nucleic acids. See, for example, Lewis (1992)Genetic Engineering News 12(9):1; Guatelli et al. (1990) Proc. Natl.Acad Sci USA 87:1874-1878; and Weiss (1991) Science 254:12921293.

Methods of determining genotype include, but are not limited to, directnucleotide sequencing, dye primer sequencing, allele specifichybridization, allele specific restriction digests, mismatch cleavagereactions, MS-PCR, allele-specific PCR, and commercially available kitssuch as those for the detection of cytochrome P450 variants (TAG-ITTMkits are available from Tm Biosciences Corporation (Toronto, Ontario).See, Stoneking et al, 1991, Am. J. Hmn. Genet. 48:370-382; Prince et al,2001, Genome Res. 11(I): 152-162; and Myakishev et al, 2001, Genome11(1):163-169.

Additional methods of determining genotype include, but are not limitedto, methods involving contacting a nucleic acid sequence correspondingto one of the loci of interest or a product of such a locus with aprobe. The probe is able to distinguish a particular form of the gene orthe gene product, or the presence of a particular variance or variancesfor example by differential binding or hybridization. Thus, exemplaryprobes include nucleic acid hybridization probes, peptide nucleic acidprobes, nucleotide-containing probes that also contain at least onenucleotide analog, and antibodies, such as monoclonal antibodies, andother probes. Those skilled in the art are familiar with the preparationof probes with particular specificities. One of skill in the art willrecognize that a variety of variables can be adjusted to optimize thediscrimination between variant forms of a gene including changes in saltconcentration, pH, temperature, and addition of various agents thataffect the differential affinity of base pairing (see Ausubel et al,eds. (1995) Current Protocols in Molecular Biology, (Greene Publishingand Wiley-Interscience, New York).

The exemplary computerized methods and/or computer-assisted methods(including software algorithms) of the invention may employ thefollowing rationale. The pharmacokinetic characteristics of a compound,particularly a neuropsychiatric drug, affect the initial dose of acompound more than the compound's pharmacodynamic properties. Acompound's pharmacokinetic profile is a dynamic summation of itsabsorption, distribution, metabolism, and excretion. Genetic differencesin drug metabolizing enzymes (DME) that affect enzyme activity and thusdrug metabolism constitute a major component of most compounds'pharmacokinetic variability. DMEs include, but are not limited to, a)medication specific metabolizing enzymes, b) medication specifictransporters, c) medication specific receptors, d) enzymes, transportersor receptors affecting other drugs that interact with the medication inquestion ore) body functions that affect that activities of themedication in question. Most compounds' absorption, distribution, andexcretion characteristics are independent of the genetic variability inDME activity. Specific DME polymorphisms affect the metabolism of mostcompounds in a reproducible, predictable, uniform manner. Typically adetectable polymorphism in a specific DME will either have no effect orwill reduce enzyme activity. Thus, the subject will have either:

1. two functional alleles (a wild-type, normal, or extensivemetabolizer);

2. one functional allele (an intermediate metabolizer); or

3. no functional alleles (a poor metabolizer).

Additionally for certain genes, such as CYP2D6, multiple copies of thegene may be present. In such instances, the presence of more than twofunctional alleles for a particular gene correlates with an ultrarapidmetabolizer state.

Frequently more than one DMEs working either in series or in parallelmetabolize a particular compound. The effect of genetic variability foreach DME can be determined independently and combined. The inventionprovides methods of combining or integrating the genetic variabilityeffect for each DME or DMEs that function sequentially or concurrently.The methods of the invention utilize Bayesian population pharmacokineticmodeling and analysis to integrate and predict the effects of multipleDMEs on metabolism of a particular compound.

Also, the concurrent use of more than one compound can affect theactivity of a subject's DMEs. Again, the effect of genetic variabilityfor each DME can be determined independently for each compound. Thecomputerized methods and/or computer-assisted methods (includingsoftware algorithms) of the invention utilize Bayesian populationpharmacokinetic modeling and analysis to integrate and predict theeffects of multiple compounds on one or more DMEs. The methods of theinvention allow the integration of information about the geneticvariability of one or more DMEs and one or more compounds to generate anarea under the time concentration curve (AUC) value. The AUC valuereflects the amount of a particular compound accessible to a patient andis the clinically important variable.

The AUC value is determined by drug dose and patient specificpharmacokinetics. Prior to this invention, medical practice utilized a“one size fits all” approach that kept the drug dose constant. In the“one size fits all” approach, variability in pharmacokinetics amongpatients leads to variability in AUC that results in interpatientclinical variability such as side effects or variable efficacy levels.Thus the methods of the invention provide a means of selecting compounddosing regimens that provide patients with similar AUC values. Themethods of the invention integrate the number of genetic variations tobe included, the population frequency for each genetic variation, andAUC data for each genetic variation. The methods of the inventiontransforms a heterogenous population into multiple homogenoussubpopulations. Such homogenous subpopulations, suitable dosingregimens, and suitable compounds can be described in a populationprofile of the invention.

By “dosing regimen” is intended a combination of factors including“dosage level” and “frequency of administration”. An optimized dosingregimen provides a therapeutically reasonable balance betweenpharmacological effectiveness and deleterious effects. A “frequency ofadministration” refers to how often in a specified time period atreatment is administered, e.g., once, twice, or three times per day,every other day, every other week, etc. For a compound or compounds ofinterest, a frequency of administration is chosen to achieve apharmacologically effective average or peak serum level withoutexcessive deleterious effects. Thus, it is desirable to maintain theserum level of the drug within a therapeutic window of concentrationsfor a high percentage of time.

The exemplary software program of the invention employs Bayesianmethods. The Bayesian methods allow fewer drug measurements forindividual PK parameter estimation, sample sizes (e.g. one sample), andrandom samples. Therapeutic drug monitoring data, when appliedappropriately, can also be used to detect and quantify clinicallyrelevant drug-drug interactions. These methods are more informative,cost-saving, and reliable than methods relying on simply reportingresults as below, within or above a published range.

Determining a Predictive Index Called the “Simplicity Index” Definitions

The following abbreviations and definitions will be used in theconstruction of the simplicity index—the variables are grouped by commonthemes:

Preclinical Toxicity variables

-   -   1. TD50=called “50% therapeutic dose”=the dose of the medication        that results in 50% of the animals tested achieving the desired        therapeutic outcome    -   2. LD50=called “50% lethal dose”=the dose of the medication that        results in 50% of the animals tested dying    -   3. TI=called therapeutic index=the ratio of LD50/TD50=a measure        of the drug's inherent toxicity

Pharmacokinetic Variables

-   -   4. F=Bioavailability=fraction of the dose which reaches the        systemic circulation as intact drug    -   5. fu=The extent to which a drug is bound in plasma or blood is        called the fraction unbound=[unbound drug concentration]/[total        drug concentration]    -   6. f-BEMD-T=fraction of drug that is a substrate for a        drug-specific efflux transporter “T”    -   7. PTX=percentage of transporter “T” with functional        polymorphism “X”    -   8. ATA=number of functional non-wild type transporter        polymorphisms for the specific patient    -   9. MET-NonL=drug with non-linear metabolism    -   10. MET-L=drug with linear metabolism    -   11. f-MET-E=fraction of drug that is metabolized by drug        metabolizing enzyme “E”    -   12. PEX=percentage of drug metabolizing enzyme “E” with        functional polymorphism “X”    -   13. AEA=number of functional non-wild type drug metabolizing        enzyme polymorphisms for the specific patient    -   14. AUC=Total area under the plasma drug concentration-time        curve=mg*hour/L    -   15. CL=clearance=the volume of blood cleared of drug per unit        time=(liters/hour), CL=dose/AUC    -   16. CL_(cr)=creatinine clearance=the volume of blood cleared of        creatinine per unit time=(liters/hour)    -   17. MED-MD=concurrent use of medications that induce        metabolizing enzymes    -   18. MED-INH=concurrent use of medications that inhibit        metabolizing enzymes    -   19. DIET-IND=concurrent use of dietary supplements that induce        metabolizing enzymes    -   20. DIET-INH=concurrent use of dietary supplements that inhibit        metabolizing enzymes

Clinical Efficacy Variables

-   -   21. NNT-EFF=number need to treat=the number of patients who need        to be treated to reach 1 desired outcome    -   22. OR=odds ratio=a measure of the degree of association; for        example, the odds of reaching the desired outcome among the        treated cases compared with the odds of not reaching the desired        outcome among the controls    -   23. META-EFF=results from an efficacy meta-analysis of clinical        trials involving medications used to treat a neuropsychiatric        disorder

Clinical Toxicity Variables

-   -   24. NNT-TOX=number need to treat=the number of patients who need        to be treated to have 1 toxicity outcome    -   25. OR=odds ratio=a measure of the degree of association; for        example, the odds of reaching the drug toxicity among the        treated cases compared with the odds of not reaching drug        toxicity among the controls    -   26. META-TOX=results from a toxicity meta-analysis of clinical        trials involving medications used to treat a neuropsychiatric        disorder

Clinical Safety Issues

-   -   27. IDR=rate of idiosyncratic reactions

Ease of Use/Adherence Variables

-   -   28. FORM=formulation    -   29. FREQ=frequency of daily drug administration    -   30. MAT ED=maternal education level    -   31. SES=socio-economic class    -   32. TRANS=method of transportation to/from clinic

An algorithm can be used to rank the most appropriate medications for anindividual patient. The design of the algorithm requires the initialidentification of the phenotype, which provides a preliminaryidentification of the universe of possible medications. At the next stepof the algorithm, the results of the target gene analyses can besequentially entered. The algorithm that produces the predictive index(called the “simplicity index”) combines the above factors using thefollowing principles:

-   -   1. Each factor contributes differentially based on weighting and        scaling variables determined during the validation process.    -   2. The following variables contribute linearly to the final        ranking score: TI, F, fu, f-BIND-T, MET-L, f-MET-E, PEX,        CL_(CR), IDR, FORM, FREQ, MATED, SES, TRANS    -   3. The following variables contribute exponentially to the final        ranking score: ATA, MET-NonL, AEA, MED-IND, MED-INH, DIET-IND,        DIET-INH5NNT-EFF, META-EEF, NNT-TOX, META-TOX

The algorithm produces a rank list of medications based on the abovepatient specific genetic factors, non-heritable patient factors and drugspecific factors. An exemplary software tool for determining such apredictive index, called the “simplicity index,” is described in detailbelow.

Determining Initial Starting Dose

The following abbreviations and definitions will be used in thedetermination of the initial starting dose:

Abbreviations

Dp_(o)p=the perceived usual drug dosage for the general population

Extensive Metabolizers

EM=extensive metabolizer

f_(EM)=frequency of extensive metabolizers in the general population

D_(EM)=Drug dosage for extensive metabolizer subpopulation

AUC_(EM)=Area Under the Time Concentration Curve for extensivemetabolizer subpopulation

Intermediate Metabolizers

IM=intermediate metabolizer

f_(IM)=frequency of intermediate metabolizers in the general population

D_(IM)=Drug dosage for intermediate metabolizer subpopulation

AUC_(PM)=Area Under the Time Concentration Curve for intermediatemetabolizer subpopulation

Poor Metabolizers

-   -   PM=poor metabolizer    -   f PM=frequency of poor metabolizers in the general population    -   D_(PM)=Drug dosage for poor metabolizers subpopulation    -   AUC_(PM)=Area Under the Time Concentration Curve for poor        metabolizers subpopulation

The following section describes how the dosing for the more homogeneoussubgroups is determined; the dosing results are expressed as a fractionof the clinician's usual heterogeneous whole group dosages.

For any one specific polymorphic DME (assuming all other relevantpolymorphic DME have normal activity), the usual drug dose seen in apopulation is the weighted summation of the drug dosages in each geneticdifferent subpopulation expressed in equation 1: (See Kirchheiner ActaPsychiatr Scand 2001:104: 173-192 BUT note authors made mistake innon-numbered equation between Equations 1 and 2, page 178):

Dpop=f _(EM) *D _(EM) +fM*DIM+f _(PM) *DPM  (Equation 1)

Assuming the goal is to maintain the same AUC for all threesubpopulations of patients, the following subpopulation dosingrelationships hold:

D _(PM) =D _(EM)*(AUC _(EM) /AUC _(PM)) OR D _(PM) =D _(EM) *R if R=(AUC_(EM) /AUC _(PM))  (Equation 2)

D _(IM) =D _(EM)*(AUC _(EM) /AUQM) OR O _(m) =D _(EM) *S if S=(AUC _(EM)/AUC _(IM))  (Equation 3)

By substituting equations 2 and 3 into equation 1, and then rearrangingthe equation to solve for the percent dose adjustment needed for eachsubgroup relative to the population dose:

D _(EM)(%)=100/(f _(EM)± f _(IM) *S±f _(PM) *R)  (Equation 4)

_(DPM)(%)=R* _(DEM)  (Equation 5)

_(DIM)(%)=S* _(DEM)  (Equation 6)

Equations 4, 5, and 6 show how the dosing for the more homogeneoussubgroups is determined and how the dosing results are expressed as afraction of the clinician's usual heterogeneous whole group dosages.

Determining “Minimal Dose Adjustment Units”

The cumulative effect of various genetic or environmentally basedalterations in DME activity will result in interpatient variability insubsequent drug dosing requirements. If the variability is large enough,then “one size fits all” dosing approach can cause noticeable toxicityin some patients and lack of efficacy in others. In this situation,clinicians alter their drug prescribing or drug dosing behavior. Wedefine the smallest clinically relevant dosing change used by cliniciansto compensate for this interpatient variability as the “minimal doseadjustment unit” (MDA unit).

The MDA unit for neuropsychiatric drugs is 20%. This means that aclinician will alter their dosing of neuropsychiatric medications inresponse to specific information if the dosing change is 20% or greater.Perturbations that either singly or in combination suggest a <20% changein dosing of neuropsychiatric medications are usually ignored.

MDA units are additive—so that a patient with one MDA unit from agenetic polymorphism and one MDA unit from a drug interaction needs a40% reduction in dose.

Example: The approach in the previous section leads to individualizedinitial drug dose recommendations for each of the 3 subgroups(extensive, poor and intermediate metabolizers). Each subgrouprepresents a specific number of functional alleles for the specific DME(extensive metabolizers have 2 functional, intermediate metabolizershave 1 functional and poor metabolizers have 0 functional). Theresultant dosing recommendations are expressed as percentages of theclinician's usual starting dose. It is possible to investigate theeffect of increasing numbers of non-functional alleles using these newdosing recommendations. For example, if DRχ% is the dosingrecommendation for subgroup X expressed as a percentage of theclinician's usual starting dose then the following are true:

Effect of claim 1 non-functional allele=(DR _(EM)%−DR _(IM)%)/DR _(EM)%

Effect of 2 non-functional allele=(DR _(EM)%−DR _(PM)%)/DR _(E) M %

Below is a spreadsheet (Table 3) that examines this for CYP2D6, CYP2C19and CYP2C9. The summary table below demonstrates:

-   -   a. it is apparent that each additional nonfunctional allele        alters dosing recommendation by at least 20%    -   b. there is a “genetic dose”-“dosing reduction” relationship        that appears constant across these 3 CYP450 genes. This approach        can be used to solidify the importance of subsequent DM genes        and to quantify their effect in MDA units.    -   c. 2D6 and 2C1 9 have 1 MDA unit per non-functional allele    -   d. 2C9 has 2 MDA units per non-functional allele. This implies        that drug metabolized through 2C9 have very large variability in        dosage requirements. This confirms the clinical impression about        these drugs (warfarin, phenytoin).

TABLE 3 PM IM EM UM 2D6 2D6 (%) (%) (%) (%) 2 al 1 al 2 al/1 alAntipsychotics A Atomoxetime 20 100 100 100 0.80 0.00 Psychostimulant BImipramine 28 79 131 182 0.79 0.40 1.98 Antidepressants A Perphenazin 3180 129 178 0.76 0.38 2.00 Antidepressants - TCA B doxepin 36 82 127 1730.72 0.35 2.02 Antipsychotics B maprotiline 36 82 127 173 0.72 0.35 2.02Antipsychotics B trimipramine 37 91 131 176 0.72 0.31 2.35Antipsychotics A thioridazine 40 85 126 140 0.68 0.33 2.10Antidepressants A desipramine 42 83 125 167 0.66 0.34 1.98Antidepressants A nortriptyline 53 96 119 152 0.55 0.19 2.87Antidepressants - TCA B clomipramine 60 89 117 146 0.49 0.24 2.04Antipsychotics A olanzapine 61 105 122 139 0.50 0.14 3.59Antidepressants - SSRIs A zuclopenthixol 63 90 116 142 0.46 0.22 2.04Antipsychotics A paroxetine 66 90 114 138 0.42 0.21 2.00 AntipsychoticsA venlafaxine 68 86 109 130 0.38 0.21 1.78 Antipsychotics B fluvoxamine69 93 112 131 0.38 0.17 2.26 Antipsychotics A aripiprazole 70 92 113 1340.38 0.19 2.05 Antipsychotics B amitryptiline 73 92 111 130 0.34 0.172.00 Antidepressants A flupentixol 74 86 116 146 0.36 0.26 1.40Antidepressants B mianserin 74 90 114 134 0.35 0.21 1.67 AntipsychoticsA haloperidol 76 97 107 126 0.29 0.09 3.10 Antidepressants - TCA Atrazadone 76 93 110 127 0.31 0.15 2.00 Antidepressants - SSRIs Bfluoxetine 78 94 107 120 0.27 0.12 2.23 Antidepressants - TCA A perazine86 91 110 117 0.22 0.17 1.26 Antipsychotics A risperidone 87 96 106 1160.18 0.09 1.90 Antidepressants - TCA A buproprion 90 97 104 111 0.130.07 2.00 Antidepressants - SSRIs A nefazodone 90 97 105 113 0.14 0.081.88 Count 26 26 25 Average 0.45 0.22 2.10 St. Dev. 0.20 0.10 0.48Antidepressants - SSRIs A pimozide 95 99 102 105 0.07 0.03Antidepressants - TCA B citalopram 98 100 101 102 0.03 0.01Antidepressants B sertraline 99 100 100 100 0.01 0.00 Antidepressants Alevomepromazine 100 100 100 100 0.00 0.00 Antidepressants A mirtazapine102 101 99 97 0.03 0.02 Antidepressants - SSRIs B clozapine 113 104 9484 0.02 0.11 Antidepressants - TCA B moclobemide 121 107 92 77 0.32 0.16PM IM UM 2C10 (%) (%) (%) 2 al 1 al 2 al/1 al Antidepressants - TCAtrimipramine 48 52 111 0.59 0.53 1.12 Antidepressants - TCA doxepin 4881 105 0.54 0.13 4.07 Antidepressants - TCA amitryptiline 53 81 109 0.510.26 2.00 Antidepressants moclobemide 54 82 110 0.51 0.25 2.00Antidepressants - TCA imipramine 58 83 108 0.46 0.23 2.00Antidepressants - SSRIs citalopram 61 84 108 0.44 0.22 1.96Antidepressants - TCA clomipramine 62 79 110 0.44 0.28 1.55Antidepressants - SSRIs fluoxetine 70 86 107 0.35 0.20 1.76Antidepressants - SSRIs sertraline 75 90 105 0.29 0.14 2.00Antipsychotics clozapine 78 91 104 0.25 0.13 2.00 Antipsychoticszotepine 82 93 104 0.21 0.11 2.00 Antidepressants - SSRIs fluvoxamine 9397 101 0.08 0.04 2.00 Count 12 12 12 Average 0.39 0.21 2.04 St. Dev.0.16 0.12 0.69 Antidepressants maprotiline 100 100 100 0.00 0.00Antidepressants mianserin 100 100 100 0.00 0.00 2C9 2 al 1 al 2 al/1 alAntidiabetic Agent, Sulfonylurea Amaryl 20% 70% 120% 0.83 0.42 2.00Antidiabetic Agent, Solfonylurea Glucotrol, 20% 70% 120% 0.83 0.42 2.00Glipizide Antidiabetic Agent, Sulfonylurea DiaBeta, 20% 70% 120% 0.830.42 2.00 Glucovance Angiotensin II Receptor Cozaar, Hyzaar 20% 50% 100%0.80 0.50 1.60 Antagonist Antidiabetic Agent, Sulfonylurea Diabinese,20% 50% 120% 0.83 0.58 1.43 Orinase, Tolinase Anticoagulant Coumadin 20%50% 130% 0.85 0.62 1.38 Analgesic -NSAID Celebrex 38% 70% 100% 0.65 0.302.17 Antilipemic Lescol 38% 80% 100% 0.65 0.20 3.25 AnticonvulsantDilantin 40% 70% 110% 0.64 0.36 1.75 Count 9 9 9 Average 0.77 0.42 1.95St. Dev. 0.09 0.13 0.56 20 50 120 0.38 0.58 1.43 20 50 100 0.80 0.501.60

TABLE 4 RELATIONSHIP BETWEEN NON-FUNCTIONAL ALLELES AND DOSE REDUCTIONEffect on percentage Average percentage Average percentage dosereduction dose reduction dose reduction of 2 non- if 1 non- if 2 non-functional alleles Gene functional allele functional allele compared to1 2D6 22% ± 10% 45% ± 20% 2.10% ± 0.48% (n = 26) (n = 26) (n = 25) 2C1921% ± 12% 39% ± 16% 2.04% ± 0.69% (n = 12) (n = 26) (n = 12) 2C9 42% ±13% 77% ± 9% 1.95% ± 0.56% (n = 9) (n = 9) (n = 9)

Determining Final Dosage Requirements

For some drugs, there is very little pharmacokinetic genetic variabilitybut rather clinically relevant pharmacodynamic genetic variability mostlikely at the drug's receptor. For these medications, the impact ofgenetic testing will be reflected in the final dosage requirementsinstead of the initial dosage requirements.

Studies that demonstrate this genetic-pharmacodynamic effect will becaptured in the software that encodes the calculations used to derivethe simplicity index described earlier. This invention will incorporatethis information and report not only the rank simplicity index of thepotential drug candidates but also those candidates that would require ahigher than expected dosing requirement to achieve the desire effect.

Population Models

The purpose of population pharmacokinetic modeling is to describe thestatistical distribution of pharmacokinetic parameters in the populationunder study and to identify potential sources of intra- andinter-individual variability among patients. Population modeling is apowerful tool to study if, and to what extent, demographic parameters(e.g. age, weight, and gender), pathophysiologic conditions (e.g. asreflected by creatinine clearance) and pharmacogenetic variability caninfluence the dose-concentration relationship. A populationpharmacokinetic analysis is robust, can handle sparse data (such astherapeutic drug monitoring data) and is designed to generate a fulldescription of the drug's PK behavior in the population. A “populationmodel” of the invention provides a description of the statisticaldistribution of at least one pharmacokinetic parameter in a givenpopulation and identifies at least on potential source of variabilityamong patients with regards to a particular compound or agent. Apopulation model of the invention may further provide mean parameterestimates with their dispersion, between subject variability andresidual variability, within subject variability, model misspecificationand measurement error for a particular compound.

An embodiment of the invention provides several novel population modelsfor predicting a medication concentration-time profile and for selectinga dosing regimen based on a user-entered target range (see examples).The computerized methods and/or computer-assisted methods (includingsoftware algorithms) of the invention employ population models such as,but not limited to, the novel population models of the invention andexternally developed population models. In an embodiment, suchexternally developed population models are adjusted or rearranged insuch a manner that they can be programmed into the software of theinvention.

In various embodiments, the computerized methods and/orcomputer-assisted methods (including software algorithms) of theinvention comprise the step of monitoring a biomarker. By “biomarker” isintended any molecule or species present in a patient that is indicativeof the concentration or specific activity of an exogenous compound inthe subject. Biomarkers include, but are not limited to, a compound, ametabolite of the compound, an active metabolite of the compound, amolecule induced or altered by administration of the compound ofinterest, and a molecule that exhibits an altered cytological, cellular,or subcellular location concentration profile in after exposure to acompound of interest. Methods of monitoring biomarkers are known in theart and include, but are not limited to, therapeutic drug monitoring.Any method of monitoring a biomarker suitable for the indicatedbiomarker known in the art is useful in the practice of the invention.

Exemplary computerized methods and/or computer-assisted methods(including software algorithms) of the invention use data generated bytherapeutic drug monitoring (TDM). TDM is the process of measuring oneor more concentrations of a given drug or its active metabolite(s) inbiological sample such as, but not limited to, blood (or in plasma orserum) with the purpose to optimize the patient's dosing regimen. Theinvention encompasses any means of measuring one or more concentrationsof a given drug or its active metabolite(s) in a biological sample knownin the art. By “biological sample” is intended a sample collected from asubject including, but not limited to, tissues, cells, mucosa, fluid,scrapings, hairs, cell lysates, blood, plasma, serum, and secretions.Biological samples such as blood samples can be obtained by any methodknown to one skilled in the art.

The following examples are offered by way of illustration and notlimitation.

EXPERIMENTAL Example 1. Optimization of Compound Dosage in an AutisticPatient

An 11-year-old boy with autism was started on risperidone (Risperdal®)therapy, at 0.5 mg two times a day. The patient's pressured speech andlabile mood did not improve with time. The lack of efficacy could be dueto insufficient coverage or to non-compliance. The patient's dosingregimen was analyzed by the methods of this invention.

Step 1 Dose Appropriateness Analysis.

The patient demographic data (age, sex, weight) and the risperidone doseand times of administration were entered into the program. A populationmodel was selected. The population model selected was a Risperidonemodel based on data of pediatric psychiatry patients. As risperidone ismetabolized by CYP2D6, there are 3 models: one for extensivemetabolizers (EM model), one for intermediate metabolizers (IM model)and one for poor metabolizers (PM model).

The genotype of the patient was determined and found to be CYP2D6*1/*1.This genotype fit the extensive metabolizer (EM model). The patient'sdata and the genotype were analyzed by an algorithm of the invention anda drug concentration profile for the patient was generated. An exemplarypharmacokinetic model-based simulation of the risperidone concentrationtime profile based on this patient's data is shown in FIG. 2A. Theaverage concentration was predicted to be around 2 ng/mL. Thisinformation is integrated with a target drug concentration profile ortherapeutic value. The therapeutic value for risperidone ranges between3 and 10 ng/mL. Comparison of the drug concentration profile for thepatient and the target drug concentration profile indicated that if thepatient were adherent, the dose may be too low. The algorithm generatedtwo recommendations: the dose can be increased and a biomarker should bemonitored.

Step 2. Integration of Biomarker Evaluation in Recommended DosageRegimen

The risperidone dose was increased to 1 mg given twice a day (morningand evening). In addition, a biomarker evaluation was performed. Druglevels were ordered and therapeutic drug monitoring were performed. Thepre-dose level and two post dose levels (1 h after dose) and (4 h afterdose) were measured. These data were entered in the software program.The software program performed a Bayesian recalculation based on the apriori information from the model in combination with the new patientspecific information (i.e. the drug levels). Exemplary results of thisBayesian update are shown in FIG. 2B. The concentrations were not withinthe target range for the major part of the dosing interval. Depending onpatient's response this would allow for further increasing the dose. Thepharmacokinetic simulation also indicated that this patient has a ratherrapid elimination of the drug form the body. The software programgenerated several recommendations. In order to maintain the targetconcentration more frequent dosing has to be considered. Based on the Bayes pharmacokinetic estimates for this patient and given the chosentarget range the dosing regimen that best meets the criteria would be1.5 mg dosed every 8 hours. An exemplary model-based profile andsubsequent Bayesian individualization process are shown in FIG. 2C.

The above-described methods according the present invention can beimplemented on a computer system such as a personal computer, aclient/server system, a local area network, or the like. The computersystem may be portable including but not limited to a laptop computer orhand-held computer. Further the computer may be a general purpose systemcapable of executing a variety of commercially available softwareproducts, or may be designed specifically to run only the drugidentification and selection algorithms that are the subject of thisinvention. The computer system may include a display unit, a mainprocessing unit, and one or more input/output devices. The one 01: moreinput/output device may include a touchscreen, a keyboard, a mouse, anda printer. The device may include a variety of external communicationinterfaces such as universal serial bus (USB), wireless, including butnot limited to infrared and RF protocols, serial ports and parallelports. The display unit may be any typical display device, such as acathode-ray tube, liquid crystal display, or the like.

The main processing unit may further include essential processing unit(CPU) in memory, and a persistent storage device that are interconnectedtogether. The CPU may control the operation of the computer and mayexecute one or more software applications that implement the steps of anembodiment of the present invention. The software applications may bestored permanently in the persistent storage device that stores thesoftware applications even when the power is off and then loaded intothe memory when the CPU is ready to execute the particular softwareapplication. The persistent storage device may be a hard disk drive, anoptimal drive, a tape drive or the like. The memory may include a randomaccess memory (RAM), a read only memory (ROM), or the like.

Exemplary Simplicity Index Software Tool

As introduced above an algorithm used to construct the drug predictiveindex (“simplicity index”) utilizes an initial identification of thedisease phenotype (e.g. epilepsy, depression, etc.), which provides apreliminary identification of the universe of possible medications forthat condition. An exemplary software tool for producing the simplicityindex uses linear algebra computational science to integrate diseasespecific evidence based medicine data, drug specific basic pharmacologycharacteristics, patient specific advanced pharmacology principles, andpatient specific environmental and genetic factors to produce a rankingof potential medications for an individual patient based on thesefactors. There are separate algorithms for each disease phenotype butthe algorithms can be run simultaneously. Further, in the exemplaryembodiment, there are three components used to produce the final rankingscore: a disease matrix, a patient vector and a weighting vector. Eachof the five factors and three components will be defined below followedby an example with a sample output. The output contains both the drugpredictive index and an adherence score.

Definitions Disease Specific Evidence Based Medicine Data

Disease specific evidence based medicine data consists of diseasespecific efficacy and tolerability data for potentially effectivemedications. This disease specific efficacy and tolerability data mayexist for age or disease subgroups; each age or disease subgroup isconsidered separately. For example in epilepsy, evidence based dataexists for five age groups (neonates, infants, children, adults, andelderly adults) along with four disease subgroups (partial onsetseizures, generalized tonic clonic seizures, absence seizures, andmyoclonic seizures). In this example, there would be a maximum of 20separate evidence based data sets covering all age-seizure typecombinations.

The first step in the evidence based approach is to identify allrelevant scientific information about the efficacy and tolerability ofany potential therapeutic modality (medical, surgical or dietary).Articles are identified through multiple methods including, but notlimited to, electronic literature searches of the medical literature,hand searches of major medical journals, the Cochrane library ofrandomized controlled trials, and the reference lists of all studiesidentified from the electronic literature searches. These articles mayinclude, but are not limited to, randomized control trials,nonrandomized controlled trials, case series, case reports, and expertopinions. Supplementary data is found in package inserts of individualdrugs.

The data in each article is evaluated for drug specific efficacy andtolerability data. The analysis is performed using the grading systemused by the national scientific organization associated with thatspecialty. If there is no national scientific organization associatedwith the specialty then the default grading system is the AmericanAcademy of Neurology evaluation system. After the evidence basedanalysis is complete, the efficacy and tolerability data for eachpotential drug (stratified by age and disease subgroup) is summarizedaccording to the following Table 5 using a scale from +1 to −1.

TABLE 5 DRUG SCORING SYSTEM FOR EFFICACY AND TOLERABILITY DATA Efficacyor Tolerability score Type of data (shown for efficacy only) 1.0 FDAindication for condition 0.9 Evidence Based Guideline Level Arecommendation 0.9 Meta-analysis evidence of efficacy 0.7 Evidence BasedGuideline Level B recommendation 0.7 RCT evidence better efficacy thananother drug or placebo 0.3 Evidence Based Guideline Level Crecommendation 0.3 non RCT clinical trial evidence of efficacy 0.3Expert opinion - drug is efficacious 0.0 No data −0.3 Expert opinion -evidence of worsening −0.3 non RCT clinical trial evidence of worsening−0.7 RCT evidence worse efficacy than another drug or placebo −0.9Meta-analysis evidence of lack of efficacy or worsening −0.9 EvidenceBased Guideline evidence of lack of efficacy or worsen −1.0 FDAcontraindication for condition

Drug Specific Basic Pharmacology Characteristics

Drug specific basic pharmacology characteristics are evaluated in threecategories: Preclinical toxicity, fundamental clinical pharmacokineticvariables and drug safety. An example in the preclinical toxicitycategory is a drug's therapeutic index. This is defined as the ratio ofLD50/TD50 where TD50 is the dose of the medication that results in 50%of the animals tested achieving the desired therapeutic outcome whileLD50 is the dose of the medication that results in 50% of the animalstested dying. Fundamental clinical pharmacokinetic variables include,but are not limited to,

-   -   i) a drug's bioavailability (fraction of the dose which reaches        the systemic circulation as intact drug),    -   ii) the fraction of the drug circulating unbound (defined by the        extent to which a drug is bound in plasma or blood=[unbound drug        concentration]/[total drug concentration]),    -   iii) the type of metabolism the drug undergoes (whether linear        or non linear),    -   iv) the type of elimination the drug undergoes (e.g. percentage        of drug renally excreted or hepatically metabolized) and    -   v) the drug's half-life.        Drug safety includes, but is not limited to, the risk of life        threatening side effects (idiosyncratic reactions) and the risk        of teratogenicity. For each drug under consideration, each        variable in the three categories is scored on a scale from +1        (most favorable) to −1 (most unfavorable).

Patient Specific Advanced Pharmacology Factors

Patient specific advanced pharmacology factors include i) bidirectionalpharmacokinetic or pharmacodynamic drug-drug interactions and ii)bidirectional pharmacodynamic drug-disease interactions. Apharmacokinetic drug-drug interaction is considered potentiallyclinically significant if there is a documented interaction that showsone drug either induces or inhibits the activity of a specific enzymeassociated with the metabolism of the other drug by ≥20%. Onlyconcomitant medications actually being taken at the time of the analysisare considered in the analysis. For drug-disease interactions, the word“diseases” refers to all forms of altered health ranging from singleorgan dysfunction (e.g. renal failure) to whole body illness (e.g.systemic lupus erythematosus). The potential for drug-drug ordrug-disease interactions is evaluated on a scale from +1 (mostfavorable) to −1 (most unfavorable).

To clarify using an example: In a specific patient, assume drug A isbeing evaluated for use in disease D. The patient is currently takingoral contraceptives, a statin for hypercholesterolemia and isoverweight. To evaluate the “Patient specific advanced pharmacologyfactors” for drug A for this patient there are 8 potential drug-druginteractions and 4 potential drug-disease interactions to evaluate: i)pharmacokinetic effect of drug A on oral contraceptives, ii)pharmacokinetic effect of oral contraceptives on drug A, iii)pharmacokinetic effect of drug A on statin medications, iv)pharmacokinetic effect of statin medication on drug A, v)-viii) the samefour combinations mentioned previously but examining the pharmacodynamicinteractions between drugs, ix) pharmacodynamic effect of drug A onhypercholesterolemia, x) pharmacodynamic effect of hypercholesterolemiaon drug A, xi) pharmacodynamic effect of drug A on weight, xii)pharmacodynamic effect of weight on drug A. If Drug A has i) aclinically significant negative effect on statin pharmacokinetics andii) causes weight gain then Drug A would receive a score of −1 for thesetwo assessments and a score of 0 for the remaining 10 evaluations. Thisapproach is repeated for each drug under consideration (e.g. drugs B, C,. . . etc.).

Patient Specific Environmental Factors

Patient specific environmental factors involve unidirectional,pharmacokinetic or pharmacodynamic, drug-environment interactions.Unidirectional refers to the effect of the environmental agent on thedrug. A pharmacokinetic drug-environment interaction is consideredpotentially clinically significant if there is a documented interactionthat shows the environmental agent either induces or inhibits theactivity of a specific enzyme associated with the metabolism of the drugby ≥20%. A pharmacodynamic drug-environment interaction is consideredpotentially clinically significant if there is a documented interactionthat shows the environmental factor alters (either positively ornegatively) the action of the drug by ≥20%. Only environmental factorsoccurring at the time of the analysis are considered in the analysis.For drug-environment interactions, the word “environment” refers to allforms of exposure ranging from food (grapefruit juice) to herbal/vitaminsupplements (e.g. St. Johns wort) to voluntary toxic exposures (e.g.smoking or alcohol) to involuntary toxic exposures (second hand smoke,pesticides). The potential for drug environment interactions isevaluated on a scale from +1 (most favorable) to −1 (most unfavorable).

Patient Specific Genetic Factors

Patient specific genetic factors involve unidirectional, pharmacokineticor pharmacodynamic, drug-gene interactions. Unidirectional refers to theeffect of the genetic variation on the pharmacokinetic orpharmacodynamic action of the drug. A pharmacokinetic drug-geneinteraction is considered potentially clinically significant if there isa documented interaction that shows the genetic factor either increasesor reduces the activity of a specific enzyme associated with themetabolism of the drug by ≥20%. A pharmacodynamic drug-gene interactionis considered potentially clinically significant if there is adocumented interaction that shows the genetic factor alters (eitherpositively or negatively) the action of the drug by ≥20%. For drug-geneinteractions, the word “gene” refers to all forms of genetic variabilityincluding DNA variability, mRNA variability, protein alterations ormetabolite alterations. The potential for drug-gene interactions isevaluated on a scale from +1 (most favorable) to −1 (most unfavorable).

Disease Matrix

An example (very small) segment of a disease matrix is provided in FIG.3. The disease matrix includes column headings for distinct treatmentmodalities (e.g. medication, therapy, surgery, dietary plan, etc.) whilethe rows are distinct factors from the five categories listed above:disease specific evidence based medicine data, drug specific basicpharmacology characteristics, patient specific advanced pharmacologyprinciples, patient specific environmental and patient specific geneticfactors. The value in each cell in the matrix ranges from +1 (favorablequality/result) to −1 (unfavorable quality/result).

Referring to the example disease matrix segment in FIG. 3, the firstcolumn 10 lists the specific factor to be evaluated for a list ofspecific treatments and/or drugs; column 12 provides the category forthe specific factor; and columns 14-20 provide the specific diseasematrix values that the specific factor associates with a specific drugor treatment. For example, the factor of Row 8, “Pharmacokinetics(metabolism),” is listed in the “Basic pharmacology” category and has awide variance of matrix values or scores depending upon the proposeddrug or treatment: carbamazepine has a −0.5 matrix value; phenobarbitalhas a 1.0 matrix value; phenytoin has a −1.0 matrix value; andtopiramate has a 1.0 matrix value. As another example, the factor of Row23, “Patient is a CYP2C9 poor metabolizer,” is listed in the “Geneticfactors” category and also has a variance of matrix scores dependingupon the proposed drug or treatment: carbamazepine has a −0.3 matrixvalue; phenobarbital has a −1.0 matrix value; phenytoin has a −1.0matrix value; and topiramate has a 0.0 matrix value.

Patient Vector Column (Matrix)

A patient vector is constructed for each individual patient. In theexemplary embodiment, the patient vector is a column (not shown in FIG.3) of the disease matrix. Optionally, the patient vector may be a 1 by Nmatrix, where N is the number of distinct factors for that particulardisease algorithm taken from the five categories—listed above: diseasespecific evidence based medicine data, drug specific basic pharmacologycharacteristics, patient specific advanced pharmacology principles,patient specific environmental and patient specific genetic factors. Theitems in the patient vector are determined by the response to a seriesof YES/NO/UNKNOWN questions for each of the variables considered. Thequestions are yes/no questions and the matrix enters a 0 (for no), 0.5(for unknown) or a 1 (for yes).

Weighting Vector

A weighting vector is constructed for each disease matrix. In theexemplary embodiment, the weighting vector is a column (not shown inFIG. 3) of the disease matrix. Optionally, the weighting vector is a 1by N matrix, where N is the number of distinct factors for thatparticular disease algorithm taken from the five categories listedabove: disease specific evidence based medicine data, drug specificbasic pharmacology characteristics, patient specific advancedpharmacology principles, patient specific environmental and patientspecific genetic factors. The values in the weighting vector aredetermined by either a supervised system (e.g. expert system) or anunsupervised system (e.g. neural network or an artificial intelligencesystem). The weighting is usually different for the different factors inthe disease algorithm. For example, referring back to FIG. 3, Row 2,“Child with partial seizures starting therapy” has a weight of claim1000, Row 13, “The patient has migraines/headaches” has a weight ofclaim 150, and Row 23, “Patient is a CYP2C9 poor metabolizer” has aweight of 250.

Algorithm Output

The main output of the algorithm is a ranking of all potential therapies(medications, surgeries or diet) for that specific disease ranging frommost likely to be successful (highest score) to least likely to besuccessful (lowest score). Each drug's score is the product of thepatient vector, the weighting vector and the particular drug's columnvalue in the disease matrix. The dosing for the drug is determined bythe algorithm described above. In the exemplary embodiment, the outputdisplay includes the top 5 factors contributing and the lowest 3 factordetracting from the score are included for evaluation. Above the rankingis an adherence score reflecting the likelihood the patient will adhereto the proposed treatment regimen. The determination and interpretationof this number is described in the Adherence score section.

Adherence Score

The adherence score is determined in a similar fashion to the simplicityindex: the score is the product of an “adherence matrix”, a patientvector and a weighting vector. For each disease, potential adherenceproblems are assessed using a series of approximately 10 yes/no/unknownquestions. If all questions are answered unknown then the adherencescore will be 50% implying a 50% chance the patient will adhere to thetreatment regimens. The more questions that are answered “no”, thehigher the adherence score and the greater the chance the patient willadhere to the prescribed treatment regimen. The more questions answered“yes”, the lower the adherence score and the greater the chance thepatient will not adhere to the prescribed treatment regimen.

Patient Example

-   -   History: The patient is a 7 year old male presenting with        frequent staring episodes lasting 30-60 seconds associated with        unresponsiveness, facial twitching and extreme tiredness        afterwards. He develops a funny taste in his mouth in the few        minutes before the events occur. He has had about 10 of these in        the past year with 3 in the last month. The patient does not        have depression, ADHD or anxiety but does have frequent        migraines. The patient is currently taking erythromycin for an        infection but takes no chronic medications. There is no family        history of epilepsy. The patient loves to drink grapefruit        juice. The family has insurance, no transportation problems and        no identifiable stressors.    -   Physical examination: Normal in detail except the patient is        very overweight    -   Lab tests: Electroencephalogram (EEG) shows normal background        and focal discharges in the temporal lobe. Magnetic Resonance        Imaging (MRI) of the brain is normal. Pharmacogenetic testing        shows a CYP2C9 polymorphism that makes him a poor metabolism for        drugs metabolized by CYP2C9.    -   Diagnosis: Newly diagnosed idiopathic partial epilepsy        characterized by partial onset seizures.    -   Need: Determine the best antiepileptic medications for this        specific patient.

Step 1: As can be seen if FIG. 4, after logging onto algorithmprogram—select disease—a screen will be provided in which the physicianwill select in field 22 that the patient's diagnosis is Epilepsy, but infield 24 that the patient's diagnosis is not depression.

Step 2: As can be seen if FIG. 5, a next step—enter age, gender andpuberty status—another screen will be provided in which the physicianselects in field 26 that the patient is between 2 and 18 years old, infield 28 that the patient is male and in field 30 that the patient ispre-pubertal.

Step 3: As can be seen in FIG. 6, a next step—select type of epilepsyand whether starting or on medications—another screen will be providedin which the physician selects in field 32 that the patient is a childwith partial seizures and no previous treatment. Fields 34-50 are notselected.

Step 4: As can be seen in FIG. 7, a next step—enter comorbidconditions—another screen will be provided in which the physicianselects in field 52 that the patient is overweight and in field 54 thatthe patient has migraines or headaches. Fields 56-62 are not selected.

Step 5: As can be seen in FIG. 8, a next step—enter EEG and MRI testresults—another screen will be provided in which the physician selectsin field 64 that the patient's EEG is abnormal with epileptiformdischarges and in field 66 that the patient's MRI/computed tomography(CT) shows normal cortical structure.

Step 6: As can be seen in FIG. 9, a next step—enter concomitantmedications—another screen will be provided in which the physicianselects in field 68 that the patient is trucing an antibiotic,antiviral, antifungal, antiparasitic or anti-tuberculosis (TB)medications. Fields 70-88 are not selected.

Step 7: As can be seen in FIG. 10, a next step—the enter concomitantmedications step is continued and another screen will be provided forthe physician to identify specific antibiotic, antiviral, antifungal,antiparasitic or anti-TB medications that the patient is taking. In thisexample, the physician selects in field 104 that the patient is takingerythromycin. Fields 90-102 and 106-1 14 are not selected.

Step 8: As can be seen in FIG. 11, a next step—enter environmentalfactors—another screen will be provided in which the physician selectsin field 118 that the patient drinks grapefruit juice. Fields 116 and120-120 are not selected since the patient does not smoke or drinkalcohol or green tea.

Step 9: As can be seen in FIG. 12, a next step—enter geneticfactors—another screen will be provided in which the physician selectsin field 126 that the patient CYP2C9 poor metabolism. As will beappreciated by those of ordinary skill, such genetic data may also beentered automatically with the assistance of the system that analyzesthe patient's genetic data.

Step 10: As can be seen in FIG. 13, a next step—enter adherencevariables—another screen will be provided in which the physician selectswhether the listed variables are present or not, or are unknown. In thisexample, all listed variables are selected as not being present infields 132, 136-144 and 148-150, except for fields 134 and 146, whichare selected as unknown.

Step 11: As can be seen in FIG. 14, a next step provides the output ofthe disease matrix algorithm to the physician based upon the previousinputs. As can be seen in this exemplary output, column 152 lists therecommended drugs for treating the patient, column 154 provides thescore for each drug listed, column 156 provides a filed in which thephysician can select to prescribe the drug, column 158 provides therecommended dosage for the patient, column 160 provides a bar-graphdisplay for each drug listed that provides the five most relevantfeatures in generating the score (the features are defined/explained inthe box 161 to the right), and field 162 indicates the adherencepercentage estimate for the patient. In this example, topiramate isrecommended by the algorithm for the patient, having a score of 2850 anda recommended dosage of claim 100% of the listed dosage. The patient iscalculated to have a 90% chance of adhering to the drug treatment.

CONCLUSION

Having described the invention with reference to the exemplaryembodiments, it is to be understood that it is not intended that anylimitations or elements describing the exemplary embodiment set forthherein are to be incorporated into the meanings of the patent claimsunless such limitations or elements are explicitly listed in the claims.Likewise, it is to be understood that it is not necessary to meet any orall of the identified advantages or objects of the invention discloseherein in order to fall within the scope of any claims, since theinvention is defined by the claims and since inherent and/or unforeseenadvantages of the present invention may exist even though they may notbe explicitly discussed herein.

Finally, it is to be understood that it is also within the scope of theinvention to provide any computer, computer-system and/or computerizedtool as is known by one of ordinary skill in the art that is designed,programmed or otherwise configured to perform any of the above-discussedmethods, algorithms or processes.

All publications, patents, and patent applications mentioned in thespecification are indicative of the level of those skilled in the art towhich this invention pertains. All publications, patents, and patentapplications are herein incorporated by reference to the same extent asif each individual publication or patent application was specificallyand individually incorporated by reference.

1. A computerized method and/or computer-assisted method of selecting adosing regimen for a patient the method comprising the steps of: (a)integrating patient data with patient associated genotype information;(b) generating a drug concentration profile for the patient; (c)integrating the drug concentration profile and the target drugconcentration profile; and (d) providing a dosing regimen for a firstcompound likely to result in the target drug concentration profile inthe subject. 2.-11. (canceled)
 12. A computerized method and/orcomputer-assisted method of selecting a dosing regimen for a patient themethod comprising the steps of: (a) generating statistical populationmodels of drug interactions for a plurality of genotypes; (b) obtainingpatient associated genotype information; (c) establishing a dosingregimen by applying the genotype information against the populationmodels. 13-14. (canceled)
 15. A computerized method and/orcomputer-assisted method for selecting one or more drugs for a patientcomprising the steps of: identifying the phenotype; providing a firstplurality of possible medications based upon the identified phenotype;calculating a ranked list or a predictive index of medications from thefirst plurality of medications based upon, at least in part, patientspecific genetic factors, non-heritable patient factors and drugspecific factors. 16.-26. (canceled)
 27. A computerized method and/orcomputer-assisted method for selecting a starting dose of a medicationfor a patient comprising the steps of: for a given medication,determining if the patient is an extensive metabolizer for themedication, an intermediate metabolizer for the medication, or a poormetabolizer for the medication; calculating the starting dose basedupon, at least in part, a usual drug dose for a given population(D_(pop)), the frequency of extensive metabolizers in the givenpopulation (f_(E) ^(u)), the frequency of intermediate metabolizers inthe given population (fi_(M)) and/or the frequency of poor metabolizersin the general population (fp_(M)); and determining a minimal doseadjustment unit for the medication based, at least in part, upon thepatient's genetic information. 28.-39. (canceled)
 40. A method ofselecting a medication for a patient suffering from a neuropsychiatricdisease or disorder comprising: obtaining an individualized medicationreport for the patient, the report comprising at least one group ofmedications, wherein the report is generated by: (a) providing a firstset of possible medications for the patient comprising at least onemedication selected from propranolol, diazepam, alprazolam andrisperidone, selection of the first set of medications determined basedon the patient's phenotype; (b) calculating a score based on thepatient's genotype for each of the possible medications, the patient'sgenotype having been determined by a genotyping assay for CYP genescomprising 1A2, 2B6, 2C9, 2C19, 2D6 and 3A4 using a biological sampleobtained from the patient; and (c) providing at least one group ofpossible medications based on the score of each medication; andselecting, from the individualized medication report, a medication basedon the report.
 41. The method of claim 40, wherein a score is calculatedfor each of propranolol, diazepam, alprazolam and risperidone.
 42. Themethod of claim 40, wherein the patient's genotype is determined bygenotyping one or more further genes selected from DAT1, SLC6A3, DRD1,DRD2, DRD3, DRD4, DRD5, TPH, 5-HTTR, HTR1A, HTR1B, HTR1D, HTR2A, HTR2Cand COMT.
 43. The method of claim 40, wherein the group of possiblemedications includes medications selected from one or more of theclasses of medications consisting of antidepressants, antipsychotics,and mood elevating or stabilizing agents.
 44. The method of claim 40,wherein the report is generated by a method comprising calculating thescore based on the patient's genotype and a non-heritable patientfactor.
 45. The method of claim 44, wherein the non-heritable patientfactor comprises toxic exposure.
 46. The method of claim 45, wherein thetoxic exposure is smoke.
 47. The method of claim 45, wherein the toxicexposure is alcohol.
 48. The method of claim 44, wherein the report isgenerated by a method further comprising obtaining or having obtainedpatient information from the patient regarding the non-heritable patientfactor, and calculating the score based on the patient's genotype andthe patient information regarding the non-heritable patient factor. 49.The method of claim 40, additionally comprising administering theselected medication to the patient.
 50. The method of claim 40,additionally comprising determining a starting dose of the selectedmedication for administration to the patient.
 51. The method of claim50, additionally comprising administering the starting dose of theselected medication to the patient.
 52. The method of claim 40, whereinthe genotyping assay for CYP genes indicates catalytic activity ofproteins encoded by 1A2, 2B6, 2C9, 2C19, 2D6 and 3A4.
 53. The method ofclaim 52, wherein the calculated score for each of the possiblemedications indicates the patient's ability to metabolize each of thepossible medications.
 54. The method of claim 53, wherein selecting themedication based on the report or determining a starting dose of theselected medication for administration to the patient is based on thecalculated score for each of the possible medications.